Optimal adaptive nonpharmaceutical interventions to mitigate the outbreak of respiratory infections following the COVID-19 pandemic: a deep reinforcement learning study in Hong Kong, China

doi:10.1093/jamia/ocad116

	Optimal adaptive nonpharmaceutical interventions to mitigate the outbreak of respiratory infections following the COVID-19 pandemic: a deep reinforcement learning study in Hong Kong, China
	Yao, Yao 1; Zhou, Hanchu 1; Cao, Zhidong2 ; Zeng, Daniel Dajun2 ; Zhang, Qingpeng 3,4,5
发表期刊	JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
ISSN	1067-5027
	2023-06-26
页码	9
通讯作者	Zhang, Qingpeng(qpzhang@hku.hk)
摘要	Background Long-lasting nonpharmaceutical interventions (NPIs) suppressed the infection of COVID-19 but came at a substantial economic cost and the elevated risk of the outbreak of respiratory infectious diseases (RIDs) following the pandemic. Policymakers need data-driven evidence to guide the relaxation with adaptive NPIs that consider the risk of both COVID-19 and other RIDs outbreaks, as well as the available healthcare resources. Methods Combining the COVID-19 data of the sixth wave in Hong Kong between May 31, 2022 and August 28, 2022, 6-year epidemic data of other RIDs (2014-2019), and the healthcare resources data, we constructed compartment models to predict the epidemic curves of RIDs after the COVID-19-targeted NPIs. A deep reinforcement learning (DRL) model was developed to learn the optimal adaptive NPIs strategies to mitigate the outbreak of RIDs after COVID-19-targeted NPIs are lifted with minimal health and economic cost. The performance was validated by simulations of 1000 days starting August 29, 2022. We also extended the model to Beijing context. Findings Without any NPIs, Hong Kong experienced a major COVID-19 resurgence far exceeding the hospital bed capacity. Simulation results showed that the proposed DRL-based adaptive NPIs successfully suppressed the outbreak of COVID-19 and other RIDs to lower than capacity. DRL carefully controlled the epidemic curve to be close to the full capacity so that herd immunity can be reached in a relatively short period with minimal cost. DRL derived more stringent adaptive NPIs in Beijing. Interpretation DRL is a feasible method to identify the optimal adaptive NPIs that lead to minimal health and economic cost by facilitating gradual herd immunity of COVID-19 and mitigating the other RIDs outbreaks without overwhelming the hospitals. The insights can be extended to other countries/regions.
关键词	Covid-19 reinforcement learning artificial intelligence machine learning mathematical modelling infectious diseases
DOI	10.1093/jamia/ocad116
收录类别	SCI
语种	英语
资助项目	Research Grants Council of the Hong Kong Special Administrative Region, China[11218221] ; Research Grants Council of the Hong Kong Special Administrative Region, China[C7154-20GF] ; Research Grants Council of the Hong Kong Special Administrative Region, China[C7151-20GF] ; Research Grants Council of the Hong Kong Special Administrative Region, China[C1143-20GF]
项目资助者	Research Grants Council of the Hong Kong Special Administrative Region, China
WOS研究方向	Computer Science ; Health Care Sciences & Services ; Information Science & Library Science ; Medical Informatics
WOS类目	Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications ; Health Care Sciences & Services ; Information Science & Library Science ; Medical Informatics
WOS记录号	WOS:001016260200001
出版者	OXFORD UNIV PRESS
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/53568
专题	舆论大数据科学与技术应用联合实验室
通讯作者	Zhang, Qingpeng
作者单位	1.City Univ Hong Kong, Sch Data Sci, Hong Kong, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China 3.Univ Hong Kong, Musketeers Fdn Inst Data Sci, Hong Kong, Peoples R China 4.Univ Hong Kong, LKS Fac Med, Dept Pharmacol & Pharm, Hong Kong, Peoples R China 5.Univ Hong Kong, Musketeers Fdn Inst Data Sci, LKS Fac Med, Dept Pharmacol & Pharm, Hong Kong, Peoples R China
推荐引用方式 GB/T 7714	Yao, Yao,Zhou, Hanchu,Cao, Zhidong,et al. Optimal adaptive nonpharmaceutical interventions to mitigate the outbreak of respiratory infections following the COVID-19 pandemic: a deep reinforcement learning study in Hong Kong, China[J]. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION,2023:9.
APA	Yao, Yao,Zhou, Hanchu,Cao, Zhidong,Zeng, Daniel Dajun,&Zhang, Qingpeng.(2023).Optimal adaptive nonpharmaceutical interventions to mitigate the outbreak of respiratory infections following the COVID-19 pandemic: a deep reinforcement learning study in Hong Kong, China.JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION,9.
MLA	Yao, Yao,et al."Optimal adaptive nonpharmaceutical interventions to mitigate the outbreak of respiratory infections following the COVID-19 pandemic: a deep reinforcement learning study in Hong Kong, China".JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION (2023):9.